Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography

Wei, Yuanyan; Tong, Xinhua; Chen, Gang; Liu, Deqiang; Han, Zhenfeng

doi:10.3390/agriculture9070150

Cited by 10 publications

(9 citation statements)

References 34 publications

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“…e main contribution of the fine-grained sentiment analysis method is to extract the corresponding features by syntactic analysis and to compare the experiments with the TF-IDF benchmark model, and their proposed model improves the accuracy (precision), recall (recall), and F1-score in positive or negative evaluations. Wei et al [4,5] extracted the features of Chinese hotel reviews by word embedding and put them into classifiers plain Bayesian (NB), support vector machine (SVM), and CNN for comparison, where SVM performed the best in classification. e word embedding approach can extract key information and hierarchical information of words from the comment text, but it cannot extract the information of emotions expressed in the words, so fusing the two features can express the information in the comment more comprehensively.…”

Section: Introductionmentioning

confidence: 99%

Emotion Analysis of Literary Works Based on Attentional Mechanisms and the Fusion of Two-Channel Features

Han

2022

Scientific Programming

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In order to analyze sentiment data of foreign literary works, this paper proposes an algorithm for sentiment classification of literary works. We do this by fusing different features of literary works, which in turn captures more feature information for the classifier. As traditional word embedding models are difficult to achieve fusion with the sentiment features of literary works, we consider a multifeature fusion approach of word embedding features and lexical features of literary works. A two-channel and single-channel comparison is also used to analyze the classification accuracy based on the two feature fusion methods, and a parallel CNN and BiLSTM-attention two-channel neural network model proposed. Finally, the proposed model was evaluated using a real dataset of sentiment reviews of literary works and compared with different classification algorithms in the experiments. The experimental results show that the new hybrid approach has better classification accuracy, recall, and F1 metrics. The proposed methodology is an important guide for the creation of literary works and their screenplays, as it can be used to judge whether a work appeals to readers and, importantly, can also be considered as one of the criteria for the success of a film adaptation of a literary work.

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Section: Introductionmentioning

confidence: 99%

Emotion Analysis of Literary Works Based on Attentional Mechanisms and the Fusion of Two-Channel Features

Han

2022

Scientific Programming

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“…Over the past years, the immediate impact of the pandemic on the agricultural system has been confirmed in a number of studies, including the loss of the workforce due to Remote Sens. 2024, 16, 1035 2 of 17 infection or quarantine [2], shortages of cultivation supplies [3], restricted transportation and trade [4,5], and disruptions to retail business [6]. However, the long-term impact of the crisis on agricultural systems remains to be well understood.…”

Section: Introductionmentioning

confidence: 99%

“…Recent sensor systems offer an even higher spatial resolution than Landsat, such as Sentinel-2, with 10 m visible and near-infrared image bands. These free and dense image time series have demonstrated the potential to capture crop phenology, i.e., the physiological development stages of plant growth from planting to harvest, and hence map crop types [15,16]. This helps address the classic challenge of high spectral similarities among diverse crop types when using single-date imagery.…”

Section: Introductionmentioning

confidence: 99%

Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand

Chen,

Hammelman,

Anantsuksomsri

et al. 2024

Remote Sensing

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This study aims to understand the spatiotemporal changes in patterns of tropical crop cultivation in Eastern Thailand, encompassing the periods before, during, and after the COVID-19 pandemic. Our approach involved assessing the efficacy of high-resolution (10 m) Sentinel-2 dense image time series for mapping smallholder farmlands. We integrated harmonic regression and random forest to map a diverse array of tropical crop types between summer 2017 and summer 2023, including durian, rice, rubber, eucalyptus, oil palm, pineapple, sugarcane, cassava, mangosteen, coconut, and other crops. The results revealed an overall mapping accuracy of 85.6%, with several crop types exceeding 90%. High-resolution imagery demonstrated particular effectiveness in situations involving intercropping, a popular practice of simultaneously growing two or more plant species in the same patch of land. However, we observed overestimation in the majority of the studied cash crops, primarily those located in young plantations with open tree canopies and grass-covered ground surfaces. The adverse effects of the COVID-19 pandemic were observed in specific labor-intensive crops, including rubber and durian, but were limited to the short term. No discernible impact was noted across the entirety of the study timeframe. In comparison, financial gain and climate change appeared to be more pivotal in influencing farmers’ decisions regarding crop cultivation. Traditionally dominant crops such as rice and oil palm have witnessed a discernible decline in cultivation, reflecting a decade-long trend of price drops preceding the pandemic. Conversely, Thai durian has seen a significant upswing even over the pandemic, which ironically served as a catalyst prompting Thai farmers to adopt e-commerce to meet the surging demand, particularly from China.

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“…Almost all rice areas in China are irrigated, which makes China's production even higher [3]. A reliable and accurate rice classification map is an important prerequisite for spatiotemporal rice monitoring and yield estimation [4,5], and it is also an important data source for food policy formulation and food security assessment [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Rice Mapping Using a BiLSTM-Attention Model from Multitemporal Sentinel-1 Data

Sun

Zhang

et al. 2021

Agriculture

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Timely and accurate rice distribution information is needed to ensure the sustainable development of food production and food security. With its unique advantages, synthetic aperture radar (SAR) can monitor the rice distribution in tropical and subtropical areas under any type of weather condition. This study proposes an accurate rice extraction and mapping framework that can solve the issues of low sample production efficiency and fragmented rice plots when prior information on rice distribution is insufficient. The experiment was carried out using multitemporal Sentinel-1A Data in Zhanjiang, China. First, the temporal characteristic map was used for the visualization of rice distribution to improve the efficiency of rice sample production. Second, rice classification was carried out based on the BiLSTM-Attention model, which focuses on learning the key information of rice and non-rice in the backscattering coefficient curve and gives different types of attention to rice and non-rice features. Finally, the rice classification results were optimized based on the high-precision global land cover classification map. The experimental results showed that the classification accuracy of the proposed framework on the test dataset was 0.9351, the kappa coefficient was 0.8703, and the extracted plots maintained good integrity. Compared with the statistical data, the consistency reached 94.6%. Therefore, the framework proposed in this study can be used to extract rice distribution information accurately and efficiently.

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Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography

Cited by 10 publications

References 34 publications

Emotion Analysis of Literary Works Based on Attentional Mechanisms and the Fusion of Two-Channel Features

Emotion Analysis of Literary Works Based on Attentional Mechanisms and the Fusion of Two-Channel Features

Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand

Rice Mapping Using a BiLSTM-Attention Model from Multitemporal Sentinel-1 Data

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